PROJECT SUMMARY/ABSTRACT The objective of this project is to create an unobtrusive, wrist-worn, cuff-less blood pressure monitor for measurement and identification of nocturnal nondipping hypertension. The investigation includes extensive validation with state-of-the-art ambulatory blood pressure monitors at nighttime in presence of heterogeneous treatment paradigms. Cardiovascular disease (CVD) is one of the major causes of ailments worldwide. Hypertension alone affects one in three adults according to the World Health Organization. Therefore, monitoring blood pressure has become a critical part of healthcare as it is known to be linked to many CVDs. Traditionally, clinical practitioners have relied on the mercury-based (or digital equivalent) inflatable cuff-based sphygmomanometer. However, the nature of the device allows for only infrequent measurements and its somewhat invasive nature and associated discomfort prohibits additional nocturnal measurements. There is certainly a value to measuring blood pressure continuously in the natural context of the user?s environment, in particular during sleep, without being disturbed by the instrument. Our proposed technology can provide a wealth of information to physicians, help identify certain short-term dynamics/variations of blood pressure, and allow effective monitoring of response to medication, among other things. Nocturnal measurements provide additional prognostic value in identifying risk. Despite these benefits, no wearable, non-invasive device for continuous blood pressure monitoring exists on the market simply because none have been reliable enough to be considered clinical grade. This project aims to develop a robust and reliable blood pressure monitor in the form of a wrist-worn device that uses bio-impedance sensors, and for the first time, demonstrate clinical grade reliability. These sensors measure pulse wave velocity (PWV) along with several other derivatives for cardiovascular parameters including heart rate and blood volume changes in arteries, which correlate with the blood pressure. The system will incorporate clever hardware design to localize underlying vasculature and focus on arterial sites for enhanced accuracy. The device will include a motion sensor to take into account the user?s movements and motion artifacts, the contact quality, and reliability of the measurements. Advanced machine learning techniques, leveraging both general and personalized models, will be developed to convert bio-impedance measurements to blood pressure. This project aims to then validate the system and analytics in both a healthy patient cohort and a hypertensive cohort, learning the impact that nocturnal ?nondipping? hypertension and anti-hypertensive treatments have on PWV/other cardiovascular correlates and blood pressure estimates. After decades of relying on the inflatable cuff- based technique, this system could represent a significant change in how we measure blood pressure.